Evidence map›Paper›PMID 40102554›Full record

ArticleScientific reports2025

Impacts of tourism on LULC and LST dynamics in district Buner and Shangla, Pakistan.

Waheed Ullah, Khalid Ahmad, Adnan Ahmad Tahir, Sajid Ullah, Muhammad Tariq Rafiq, M Abdullah-Al-Wadud

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Waheed UllahDepartment of Environmental Sciences, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, 22060, Pakistan.
Khalid AhmadDepartment of Environmental Sciences, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, 22060, Pakistan. khalidahmad@cuiatd.edu.pk.
Adnan Ahmad TahirDepartment of Environmental Sciences, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, 22060, Pakistan.
Sajid UllahDepartment of Water Resources and Environmental Engineering, Nangarhar University, Jalalabad, Nangarhar, 2600, Afghanistan. sajidjalwan@gmail.com.
Muhammad Tariq RafiqCenter for Interdisciplinary Research in Basic Sciences, International Islamic University, Islamabad, 44000, Pakistan.
M Abdullah-Al-WadudDepartment of Software Engineering, College of Computer and Information Sciences, King Saud University, 11543, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tourism activities are changing the global landscape pattern. This study attempted to estimate changes in Land Use Land Cover (LULC) and Land Surface Temperature (LST) in District Buner and Shangla, Khyber Pakhtunkhwa (KPK), Pakistan, and specifically its tourist spots. Using remote sensing data from satellites (1990-2020) and future projections (2035-2050), we applied Artificial Neural Network (ANN) and Cellular Automata Markov (CA-Markov) models to examine past and future LULC and LST dynamics across two districts including four major tourist spots (Shangla Top as tourist spot one (TS1), Bar Puran (TS2), Shahida Sar (TS3), and Daggar (TS4). The LULC classification for the whole study area (1990-2020) indicates that built-up and agricultural areas increased with a net change of +0.8% and +3.2% for the Shangla and Buner districts, respectively. The highest mean LST was found in the built-up areas. The simulation results indicate an expansion of 4.5% and 5.8% of the total built-up areas, and the LST above 31 °C will cover 76% and 88% of the total areas in 2035 and 2050, respectively. This conversion is driven by tourism activities, causing urban heat island effects (UHIs), and environmental degradation. The analysis of tourist spots (1990-2020) shows the highest change in built-up areas at Shangla Top (TS1), while the highest LST (28 °C) for the Daggar (TS4). The future simulation (2035-2050) results for tourist spots show that TS4 would have the highest LULC change in built-up areas (5.67%), and TS4 would have the highest LST (31 °C) from 65.23 to 82.20%. These findings provide an essential understandings for developing long-term tourism policies meant to moderate the environmental impact of tourism in the region.

Indexed as

Artificial neural networkCellular automata MarkovLand surface temperatureLand use land coverModelTourism

Identifiers

PMID40102554
PMCPMC11920440

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.